Evaluation of Machine-Learning Algorithms for Predicting Opioid Overdose Risk Among Medicare Beneficiaries With Opioid Prescriptions – JAMA Netw Open. – Mar 2019
Question:Can machine-learning approaches predict opioid overdose risk among fee-for-service Medicare beneficiaries?
Findings: In this prognostic study of the administrative claims data of 560 057 Medicare beneficiaries, the deep neural network and gradient boosting machine models outperformed other methods for identifying risk, although positive predictive values were low given the low prevalence of overdose episodes.
Meaning: Machine-learning algorithms using administrative data appear to be a valuable and feasible tool for more accurate identification of opioid overdose risk.
Design, Setting, and Participants
A prognostic study was conducted between September 1, 2017, and December 31, 2018. Participants (n = 560 057) included fee-for-service Medicare beneficiaries without cancer who filled 1 or more opioid prescriptions from January 1, 2011, to December 31, 2015.
Beneficiaries were randomly and equally divided into training, testing, and validation samples.
Potential predictors (n = 268), including
- health status,
- patterns of opioid use, and
- practitioner-level and regional-level factors,
were measured in 3-month windows, starting 3 months before initiating opioids until loss of follow-up or the end of observation.
deep neural network (DNN) were applied to predict overdose risk in the subsequent 3 months after initiation of treatment with prescription opioids.
The DNN classified patients into
- low-risk (76.2% [142 180] of the cohort),
- medium-risk (18.6% [34 579] of the cohort), and
- high-risk (5.2%  of the cohort)
subgroups, with only 1 in 10 000 in the low-risk subgroup having an overdose episode.
More than 90% of overdose episodes occurred in the high-risk and medium-risk subgroups, although positive predictive values were low, given the rare overdose outcome.
Conclusions and Relevance
Machine-learning algorithms appear to perform well for risk prediction and stratification of opioid overdose, especially in identifying low-risk subgroups that have minimal risk of overdose.
End of Abstract – excerpts from the full article below:
…health systems, payers, and policymakers have developed programs to identify and intervene in individuals at high risk of problematic opioid use and overdose.
It’s really noticeable about how risk stratification is being explored, decided, and implemented by these three powerful profit-driven groups:
- health systems, (medical groups, hospitals, medical labs, etc.)
- payers, (AKA insurance companies)
- policymakers (lobbyists for special interests, politicians, “expert witnesses”)
Our health and welfare are literally in the hands of entities that are bound by their corporate bylaws to maximize profit above all else.
Yet, the definition of high risk is variable, ranging from a high-dose opioid (defined using various cut points) to the number of pharmacies or prescribers that a patient visits.
These criteria, for example, determine how Medicare beneficiaries are selected into so-called lock-in programs in Medicare, also called the Comprehensive Addiction and Recovery Act (CARA) drug management programs.
Machine learning is an alternative analytic approach to handling complex interactions in large data, discovering hidden patterns, and generating actionable predictions in clinical settings. In many cases, machine learning is superior to traditional statistical techniques
Our overall hypothesis was that a machine-learning algorithm would perform better in predicting opioid overdose risk compared with traditional statistical approaches.
The objective of this study was to develop and validate a machine-learning algorithm to predict opioid overdose among Medicare beneficiaries with at least 1 opioid prescription.
Based on the prediction score, we stratified beneficiaries into subgroups at similar overdose risk to support clinical decisions and improved targeting of intervention.
We chose Medicare because of the high prevalence of prescription opioid use and the availability of national claims data and because the program will require specific interventions targeting individuals at high risk for opioid-associated morbidity
This study is ridiculously skewed from the start because Medicare patients are all “senior citizens”.
This demographic is the opposite of the people abusing opioids: they are sicker, in more pain, and don’t have the energy to pursue addictions.
- The oldest people have many more chronic and potentially painful diseases like arthritis or heart disease.
- The elderly suffer from all kinds of pains due to the unavoidable degradation of body tissues over time.
- Seniors have been the group found least likely to overdose.
The illicit drugs and combinations thereof that cause almost all of the overdoses are generally a young person’s game. You don’t see many seniors out scoring drugs on street corners.
As expected in a population with very low prevalence of the outcome, the PPV of the models was low; however, these algorithms effectively segmented the population into 3 risk groups according to predicted risk score, with three-quarters of the sample in a low-risk group with a negligible overdose rate and more than 90% of individuals with overdose captured in the high- and medium-risk groups.
The ability to identify such risk groups has important potential for policymakers and payers who currently target interventions based on less accurate measures to identify patients at high risk.
It’s scary to think pf “policymakers and payers” using artificial intelligence algorithms to make any decisions, let alone the ones that are most crucial to not just our wellbeing, but our very survival: health and money.
However, although opioid overdose represents a particularly important outcome, it is a rare outcome, especially in the Medicare population.
Well, I’m glad that was finally clarified, though only in the conclusion of this long article.
…our risk stratification strategies may more efficiently guide the targeting of opioid interventions among Medicare beneficiaries compared with exisiting measure.
Like they “efficiently guided the targeting” of doctors and patients to persecute? The military wording sounds like they’re describing a cruise missile, which gives an ominous hint about the true motivations at work here.
This strategy first excludes most (approximately 75%) prescription opioid users with negligible overdose risk from burdensome interventions like pharmacy lock-in programs and specialty referrals.
Targeting medium- and/or high-risk groups can capture nearly all (90%) overdose episodes by focusing on only 25% of the population, which greatly frees up resources for payers and patients.
Again, they’re using military words by “capturing” overdose episodes.
For those in the high- and medium-risk groups, although most will be false-positives for overdose given the overall low prevalence, additional screening and assessment may be warranted.
They state this so casually because they have no idea of the consequences to any patient who is given such a “false positive” risk score. It could prevent them from ever receiving opioids for pain relief again.
Although certainly not perfect, these machine-learning models allow interventions to be targeted to the small number of individuals who are at greater risk, and these models are more useful than other prediction criteria that have considerably more false-positives.
So now we find out that “other prediction criteria” have even “considerably more false-positives”?